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13th International Symposium on Intelligent Systems (INTELS)

作者： Demidova, L. A. Klyueva, I. A. Pylkin, A. N. Ryazan State Radio Engn Univ 59-1 Gagarina Str Ryazan 390005 Russia

The aim of this work is to improve the results of the svm classification (Support Vector Machine) by hybridizing the svm classifier with the random forest classifier (Random Forest, RF) used as the auxiliary. Specification of the classification decisions obtained on the basis of the svm classifier is performed for the objects located in the experimentally determined subareas near the hyperplane separating the classes and including both correctly and erroneously classified objects. In the case of improving the quality of the objects classification from the initial dataset, the proposed hybrid approach to the objects classification can be recommended for classification of new objects. When developing the svm classifier, the fixed default parameters values are used. A comparative analysis of the classification results obtained during the computational experiments in the hybridization of the svm classifier with two auxiliary classifiers the random forest classifier (RF classifier) and the k nearest neighbor classifier (kNN classifier), for which the parameters values are determined randomly, confirms the expediency of using of these classifiers to increase the svm classification quality. It was found that in most cases, the random forest classifier works better in terms of improving the svm classification quality in comparison with the kNN classifier. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： intelligent classification algorithm svm algorithm k-nearest neighbors algorithm random forest algorithm support vector kernel function kernel function parameter regularization parameter hybridization

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Rolling Bearing Fault Diagnosis Based on Support Vector Machine Optimized by Improved Grey Wolf algorithm

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SENSORS 2023年第14期23卷 6645-6645页

作者： Shen, Weijie Xiao, Maohua Wang, Zhenyu Song, Xinmin Zhejiang Tech Inst Econ Hangzhou 310018 Peoples R China Nanjing Agr Univ Coll Engn Nanjing 210031 Peoples R China

This study targets the low accuracy and efficiency of the support vector machine (svm) algorithm in rolling bearing fault diagnosis. An improved grey wolf optimizer (IGWO) algorithm was proposed based on deep learning and a swarm intelligence optimization algorithm to optimize the structural parameters of svm and improve the rolling bearing fault diagnosis. A nonlinear contraction factor update strategy was also proposed. The variable coefficient changes with the shrinkage factor & alpha;. Thus, the search ability was balanced at different early and late stages by controlling the dynamic changes of the variable coefficient. In the early stages of optimization, its speed is low to avoid falling into local optimization. In the later stages of optimization, the speed is higher, and finding the optimal solution is easier, balancing the two different global and local optimization capabilities to complete efficient convergence. The dynamic weight update strategy was adopted to perform position updates based on adaptive dynamic weights. First, the dataset of Case Western Reserve University was used for simulation, and the results showed that the diagnosis accuracy of IGWO-svm was 98.75%. Then, the IGWO-svm model was trained and tested using data obtained from the full-life-cycle test platform of mechanical transmission bearings independently researched and developed by Nanjing Agricultural University. The fault diagnosis accuracy and convergence value of the adaptation curve were compared with those of PSO-svm (particle swarm optimization) and GWO-svm diagnosis models. Results showed that the IGWO-svm model had the highest rolling bearing fault diagnosis accuracy and the best diagnosis convergence.

关键词： rolling bearing fault diagnosis IGWO algorithm svm algorithm

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Diagnosis of thyroid neoplasm using support vector machine algorithms based on platelet RNA-seq

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ENDOCRINE 2021年第3期72卷 758-783页

作者： Shen, Yuling Lai, Yi Xu, Dong Xu, Le Song, Lin Zhou, Jiaqing Song, Chengwen Wang, Jiadong Shanghai Jiao Tong Univ Sch Med Renji Hosp Dept Head & Neck Surg 160 Pujian Rd Shanghai 200127 Peoples R China Fun Med Pharmaceut Technol Shanghai Co Ltd RM A310115 Xinjunhuan Rd Shanghai 201100 Peoples R China

Objective To assess the capacity of support vector machine (svm) algorithms that are developed based on platelet RNA-seq data in identifying thyroid neoplasm patients and differentiating patients with thyroid adenomas, papillary thyroid cancer and metastasized papillary thyroid cancer. Methods Platelets were collected and isolated from 109 patients and 63 healthy controls. RNA-seq was performed to find transcripts with differential levels. Genes corresponding to these altered transcripts were identified using R packages. All samples were subsampled into a training set and a validation set. Two svm algorithms were developed and trained with the training set, using the genes with differential transcript levels (GDTLs) as classifiers, and validated with the validation set. GO and KEGG pathway enrichment analysis were performed using the R package clusterProfiler. Results We detected 765 GDTLs (442 up-regulated and 323 down-regulated) in platelets of patients and healthy controls. The algorithm identifying thyroid neoplasm patients achieved an accuracy of 97%, with an AUC (area under curve) of 0.998. The other algorithm differentiating patients with multiclass thyroid neoplasms had an average accuracy of 80.5%. GO analysis showed that GDTLs were strongly involved in biological processes such as neutrophil degranulation, neutrophil activation, autophagy and regulation of multi-organism process. KEGG pathway enrichment analysis revealed that GDTLs were mainly enriched in NOD-like receptor signaling pathway and pathways in endocytosis, osteoclast differentiation, human cytomegalovirus infection and tuberculosis. Conclusion Our results indicated that the combination of svm algorithms and platelet RNA-seq data allowed for thyroid neoplasm diagnostics and multiclass thyroid neoplasm classification.

关键词： Thyroid neoplasm svm algorithm Platelet RNA-seq Bioinformatics analysis

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Application of traditional cultural elements in landscape design

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年

作者： Liu, Tong Zhengzhou Univ Light Ind Eastern Int Art Coll Environm Design Dept Zhengzhou 451450 Peoples R China

Traditional Chinese cultural elements have diversified the framework of contemporary cultural studies and given new life to modern landscape design. Therefore, the use of traditional Chinese cultural elements in modern landscape design is the main task faced by landscape design designers. This paper establishes the svm-design model. An in-depth study on the use of traditional cultural elements in landscape design is launched. The calculation analysis shows that the color application of traditional cultural elements is the most widely used in the creation of landscape space, and its percentage is as high as 60.22%. The use of regional characteristics of traditional cultural elements accounted for the second highest percentage, at 24.73%. The lowest percentage of pattern features of traditional cultural elements is used in the creation of landscape space, only 15.05%. The percentage of the use of traditional cultural elements of the deformation of the exaggerated clothing method is 43%. The reason for its wide application in landscape design is that it is a deformation and exaggeration treatment of the external form of traditional cultural elements to achieve the aesthetic effect of highlighting and amplifying the traditional cultural elements.

关键词： traditional culture landscape design svm algorithm color application elemental characteristics

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Smart Machine Health Prediction Based on Machine Learning in Industry Environment

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INFORMATION 2023年第3期14卷 181页

作者： Yeruva, Sagar Gunuganti, Jeshmitha Kalva, Sravani Salkuti, Surender Reddy Kim, Seong-Cheol VNR Vignana Jyothi Inst Engn & Technol Dept CSE AIML & IoT Hyderabad 500090 India VNR Vignana Jyothi Inst Engn & Technol Dept CSE Hyderabad 500090 India Woosong Univ Dept Railroad & Elect Engn Daejeon 34606 South Korea

In an industrial setting, consistent production and machine maintenance might help any company become successful. Machine health checking is a method of observing the status of a machine to predict mechanical mileage and predict the machine's disappointment. The most often utilized traditional approaches are reactive and preventive maintenance. These approaches are unreliable and wasteful in terms of time and resource utilization. The use of system health management in conjunction with a predictive maintenance strategy allows for the scheduling of maintenance times in such a way that device malfunction is avoided, and thus the repercussions are avoided. IoT can help monitor equipment health and provide the best outcomes, especially in an industrial setting. Internet of Things (IoT) and machine learning models are quite successful in providing ongoing knowledge and comprehensive study on infrastructure performance. Our suggested technique uses a mobile application that seeks to anticipate the machine's health status using a classification method utilizing IoT and machine learning technologies, which might benefit the industry environment by alerting the appropriate maintenance team before inflicting significant harm to the system and disrupting normal operations. A comparison of decision tree, XGBoost, svm, and KNN performance has been carried out. According to our findings, XGBoost achieves higher classification accuracy compared to the other algorithms. As a result, this model is selected for creating a user-based application that allows the user to easily check the state of the machine's health.

关键词： Decision Tree algorithm IoT KNN algorithm machine health prediction predictive maintenance svm algorithm XGBoost algorithm

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Research on the UBI Car Insurance Rate Determination Model Based on the CNN-HVsvm algorithm

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IEEE ACCESS 2020年 8卷 160762-160773页

作者： Yan, Chun Wang, Xindong Liu, Xinhong Liu, Wei Liu, Jiahui Shandong Univ Sci & Technol Coll Math & Syst Sci Qingdao 266590 Peoples R China Beijing Inst Petrochem Technolog Dept Math & Phys Beijing 102617 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

With the support of Internet of Vehicles technology, UBI (Usage Based Insurance) car insurance rate determination has certain guiding significance for achieving accurate pricing of car insurance rates and satisfying the personalized needs of users. Based on the CNN (Convolutional Neural Networks) algorithm and svm (Support Vector Machine) algorithm, this paper establishes a rating model for UBI car insurance rates. The model first performs a series of operations such as convolutions, pooling and nonlinear activation function mapping using the CNN algorithm so that it can extract the features from the driving behavior data of UBI customers. Then, it introduces the Hull Vector to optimize the operating efficiency of the svm algorithm. The HVsvm (Hull Vector Support Vector Machine) algorithm classifies customers according to their driving behavior, and thus obtains UBI customer car insurance rate grades. Therefore, this paper proposes a UBI car insurance rate grade determination model based on the CNN-HVsvm algorithm. The empirical results of the model show that the CNN-HVsvm algorithm has higher discrimination accuracy in the risk rating process of UBI customer driving behavior than the CNN algorithm, BP neural network algorithm and svm algorithm;and when dealing with large training sets, it has a speed advantage over the CNN-svm algorithm. Furthermore, it is easy to realize in the process of establishing the UBI car insurance rate determination model and it has good robustness, which can adapt to diverse data sets, thus achieving better results in the car insurance rate determination process. Therefore, the CNN-HVsvm model can predict the grade of UBI car insurance users more accurately and efficiently, and the prediction results are more consistent with the actual situation, which has strong applicability and flexibility. The UBI car insurance premium rate determination model based on the CNN-HVsvm algorithm can determine driver behavior more fairly and reasonably,

关键词： Insurance Automobiles Support vector machines Industries Prediction algorithms Big Data CNN algorithm CNN-HVsvm algorithm rate grade judgment svm algorithm UBI car insurance

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Robust Vehicle Classification Using Sound Signals and Attention Module

Robust Vehicle Classification Using Sound Signals and Attent...

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International Conference on Smart Transportation and City Engineering

作者： Chi, Guowei Xu, Zijun Sowah, Edmund Li, Weiqing Nanjing Univ Sci & Technol Coll Comp Sci & Technol Nanjing Peoples R China Nanjing Univ Sci & Technol Coll Elect & Opt Engn Nanjing Peoples R China

ISBN: (数字)9781510649767

ISBN: (纸本)9781510649767;9781510649750

Classification of vehicles in urban areas is an important part of transport and traffic management. To improve the performance of car classification, many Intelligent Transport Systems (ITS) have been developed to accomplish this tedious task. Unfortunately, most of these systems are inaccurate in vehicle detection and/or classification. This paper proposes a new car classification algorithm based on vehicle acoustic signals, which adds an attention module to the original BP neural network, highlighting features in the time and frequency domain information that are more capable of distinguishing between vehicle classes, and providing better robustness in real-world environments. To evaluate the performance of the proposed algorithm on the vehicle classification task, we compare the method with the traditional svm algorithm and the BP neural network without the attention mechanism Experimental results show that the method using vehicle acoustic features combined with an attention module achieves a classification accuracy of 89.34%, which is a 12.22% improvement over the traditional svm method, and a 5.23% improvement compared to the BP neural network method without the attention module.

关键词： vehicle acoustic signals BP neural network svm algorithm

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Automated Polycystic Ovarian Syndrome Identification with Follicle Recognition 3

Automated Polycystic Ovarian Syndrome Identification with Fo...

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3rd International Conference on Signal Processing and Communication (ICPSC)

作者： Madhumitha, J. Kalaiyarasi, M. Ram, S. Sakthiya Bannari Amman Inst Technol Dept EIE Sathyamangalam Tamil Nadu India

ISBN: (纸本)9781665428644

The use of ultrasound, also known as sonography, has assisted in the identification and care of infertile patients. Ultrasound imaging of the ovary's follicles provides crucial details about the ovary, such as the type of cyst, the large range of follicles, and the size of the follicles reaction to hormonal imbalance. Image Segmentation provides additional details about the region of interest in an image and accurately identifies the object and its background from the image. However, since segmentation on ultrasound images is difficult owing to noise, identification of follicles can be made simple and efficient by combining photograph preprocessing with morphological operations. svm, KNN and Logistic Regression algorithm of Machine learning are used for classification which taking into considerations of all the specifications of PCOS Ovaries and Normal ovaries. The result obtained through the classification are compared through physical identification. The proposed hybrid method of combining all three algorithms is implemented, for which the accuracy obtained is 98%.

关键词： Polycystic Ovary Segmentation Classification svm algorithm KNN algorithm Logistic Regression

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Research on the innovation of collaborative education model of scientific research and teaching in colleges and universities based on the theory of process reengineering

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第2期8卷 1017-1028页

作者： Shi, Chen Ningbo Univ Finance & Econ Ningbo 315175 Zhejiang Peoples R China

Colleges and universities are the major sources of scientific research. They reveal how the effectiveness is related to the development of the whole society and the living standards of all employees. This article starts from the education model, based on relevant theoretical knowledge. It helps to explore and innovate the path to realize the 'college scientific research and teaching collaborative education model' within the university and construct the svm-process reengineering model to study its feasibility. According to the calculation results, the teaching results of the enterprise teaching mode are the lowest, with an average proportion of about 32.74%, the average proportion of the teaching results of the traditional teaching mode is only 42.56%, and the average proportion of the teaching results of the collaborative education is as high as 97.86%. Therefore, measures should be taken to enhance the flexibility of training content and to carry out various forms of school research, teaching and collaborative education models.

关键词： scientific research and teaching collaborative education model process reengineering theory svm algorithm teaching results

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Fall detection system based on infrared array sensor and multi-dimensional feature fusion

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MEASUREMENT 2022年 192卷 110870-110870页

作者： Yang, Yi Yang, Honglei Liu, Zhixin Yuan, Yazhou Guan, Xinping Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

In recent years, with the acceleration of the aging of the population, the safety of the elderly living alone has attracted great attention, and the falls have become one of the main factors leading to elderly casualties. In order to obtain a high precision and low cost fall detection system for the elderly, a fall detection system based on infrared array sensor and multi-dimensional feature fusion is proposed in this paper. First, we propose a new data acquisition method using infrared array sensor, which effectively enlarges the detection area. Then the personnel positioning is performed before fall detection, which can ensure real-time detection while reducing computational complexity. In addition, a sliding window algorithm is developed and four representative features of a fall are extracted from the collected data, which is fitful to the online detection. Among them, the four characteristics include the change in the center of mass of the falling process, the change in the speed, the change in the area of the person, and the change in variance. Finally, based on the refined features, the support vector machine (svm) classifier is introduced to identify falls and improve the classification accuracy. The experimental results validate that the proposed fall detection system shows good fall detection accuracy and great practicability.

关键词： Fall detection Infrared array sensor Feature extraction svm algorithm

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